Articles in 2026

The \(KJ\Gamma {\Gamma }^{{\prime} }\) model, based on ideal symmetries, has been central to realizing Kitaev quantum spin liquids (KQSLs), but real materials often break these symmetries, requiring a more general framework. Here the authors develop a generalized spin model incorporating arbitrary lattice deformations and show that strain drives topological transitions and stabilizes KQSLs in real materials.

Identifying key input features is crucial for enhancing decision-making processes in machine learning. Here, the authors introduce a method that uses spectral re-parametrization of the model’s input layer to estimate the relative importance of input components in a deep neural network, validated on synthetic data and real datasets, including stellar spectra of 985 stars used to assess metallicity.

Reconfigurable RF filters usually rely on multiple coupled resonators and many tuning elements. The authors show that difference-frequency pumping can turn the eigenmodes of a single MEMS resonator into a synthetic SSH lattice whose topological edge states create a tunable narrowband passband.

Guiding waves through complex paths typically requires carefully optimized photonic structures. Inspired by the local rules underlying spontaneous pattern formation in nature, the authors demonstrate self-organized waveguides that efficiently guide waves along arbitrary shapes without optimization.

Electric currents at material interfaces can create orbital angular momentum, but detecting this Rashba-Edelstein effect has been challenging. Here, the authors use first-principles calculations to show that, in Co/Pt bilayers, it can be detected optically through the Voigt effect, revealing a large orbital signal with a strong interfacial contribution from Pt.

Variational quantum algorithms face a practical trade-off between circuit depth and trainability, which limits the reachability of target quantum states. Here, the authors show that input-state design based on linear combinations systematically modifies the reachable-state set of a fixed ansatz, thereby enhancing target-state reachability and improving ground-state preparation accuracy across representative quantum many-body models without increasing gate cost.

While the snowdrift game, a social dilemma where individuals choose between cooperation and self-interest, has been extensively studied, the adaptive costs of cooperation remain underexplored. Here, the authors integrate the Kuramoto model into the snowdrift game, utilizing the synchronization level to characterize the degree of coordination among players, revealing explosive transitions in cooperation and synchronization that offer a nuanced understanding of game dynamics.

While tuning topological properties of quantum states can provide protection from disorder, in a crystal topology is underpinned by chemical composition and lattice symmetry that are difficult to modify. Here, the authors show that in junctions of two rhombohedral graphite crystals, sliding one with respect to the other enables smooth transition between topological and trivial electronic regimes.

Molten Carbon, existing only at extremely high temperatures and high pressures, has an exotic collective dynamic behavior. The authors reveal, via ab initio and machine-learning computer simulations, an unusual two-peak shape of longitudinal current spectral functions, which are related to two types of collective excitations.

Under compression, fluid-filled cylindrical shells, such as soda cans, exhibit localized axisymmetric corrugations which appear sequentially but are evenly spaced once the surface is fully decorated. Here, the authors demonstrate how the pattern formation process underpinning this buckling phenomenon depends on material nonlinearities, offering insights that could inform the design of resilient cylindrical structures.

Sensitivity to small changes is essential for organisms to make timely and reliable decisions, but operating near criticality also amplifies fluctuations and hinders information accumulation. Here, the authors show that when information is integrated over a finite time, as in biological readouts, the optimal sensitivity for a given integration time is achieved away from criticality, approaching criticality only as longer integration becomes available.

Network dismantling, crucial for optimizing systems like immunization and rumor control, faces challenges in integrating higher-order structures to identify key nodes. Here, the authors introduce a Higher-order Graph Neural Network framework, demonstrating superior efficiency and resilience in dismantling networks by accurately targeting minimal nodes, impacting fields from ecology to cybersecurity.

The superconducting diode effect describes non-reciprocal transport of the superconducting current and there is focus on how to engineer and apply this property practically. Here, the authors utilise a heterostructure to implement a dual-mode superconducting diode effect, where dissipationless current flows along a single direction, that can be activated by both out-of-plane and in-plane magnetic field. The dual modes share similar diode efficiency but requires different magnitude of magnetic field to be operated.

The concept of distance in graphs and hypergraphs faces challenges when extended to weighted hypergraphs due to potential inconsistencies. The authors propose a well-defined distance measure for weighted hypergraphs and demonstrate its applicability on real-world datasets, showing that the use of the measure may help to avoid the information loss typically arising when standard approaches are used.

Collective failure poses a significant challenge to sustaining cooperation in various systems. Here, the authors demonstrate that adaptively raising collective targets after success or increasing rewards after failure effectively maintains cooperation across different risk levels, offering a dynamic governance strategy with broad implications for enhancing collaborative efforts.

Criticality and percolation in dynamical systems are widely studied, yet whether they can emerge from purely deterministic interactions and control parameters remains unclear. Here, the authors reveal deterministic critical points associated with percolation and self-organized criticality in the logistic Game of Life, advancing the understanding of emergent scale invariance in deterministic systems.

Shortcuts to adiabaticity are essential for rapid state evolution, and their true power lies in restoring adiabaticity against nonadiabatic transitions. Here, the authors employ an enhanced framework that restores adiabaticity in coupled elastic waveguides by simultaneously optimizing the parameter-space path and velocity, enabling efficient wave control in compact devices.

The electrical generation of orbital angular momentum in materials has attracted significant attention due to its fundamental importance and technological potential. Here, the authors demonstrate current-induced orbital accumulation in pristine and naturally oxidized Cu films using magneto-optical Kerr effect measurements.

Long Gamma Ray Bursts (GRBs) are linked to the core collapse of massive stars, and in many cases Type Ic supernovae. Here, the authors identify that the time-evolving emission line in GRB 221009A is consistent with Doppler-boosted 56Ni decay, providing insights into supernova nucleosynthesis and relativistic jet dynamics.

Relaxor ferroelectrics (RFEs) are widely used for their excellent electrical properties rooted in polar nano regions (PNRs), yet how PNRs’ collective dynamic behavior impacts material performance is poorly understood. Taking KBT RFEs as a model, the authors identify unique PNR mesostructures, reveal their Turing instability origin, and clarify jamming effects as key to boosting their electrical properties.